When dealing with a processing system that emulates a human brain, multiple techniques have been used in the past. One is to utilize a first principals system which defines the artificial brain in the form of a plurality of rules and various hierarchical structures. For example, some linguistics systems utilize a database comprised of a plurality of linguistic rules that are used to parse a sentence into its individual parts, such as subject, verb and object and then organize sentences as groups of components into paragraphs and the paragraphs into an overall outline. Some of these systems have rules to determine the concepts behind particular sentences and paragraphs, and even generate an overall outline. A second type of system utilizes a neural network which is comprised of a plurality of weighted nodes that are interconnected with input and output nodes. The various internal nodes, referred to as activation nodes, are trained to provide an overall representation of a system. These are non-linear networks and each node has a function that is determined by training and the such. No one given node has a predefined relationship. Any type of nodal system utilized to represent a series of complex relationships typically requires a plurality of individual node, each defined in terms of activation levels, such that the “firing” of a particular node based upon an input or an event will cause the firing of sequential nodes to represent an overall concept. The use of such nodes does not lend itself to defining concepts in the discrete manner or of interconnecting such concepts in an adequate manner.